The Year of AI: 5 Things We Learned

As ChatGPT fast approaches its first birthday, we reflect on the 5 things we have taken away from the year marketers finally embraced AI.

*Spoiler: Our biggest learning has nothing to do with optimising our creative process. *

Once upon a time, when ‘midjourney’ was just another way of saying you were ‘halfway there’, Toaster launched its own AI Design Experiment (AIDE, c. 2017). We had some fun applying AI to different creative tasks, from designing book covers to optimising design layouts. It was our first true glimpse at how AI could positively impact our industry.

Fast forward 6 years, it seems there’s a new AI product launch or feature update happening every second. So we embarked on a new experiment: instead of building our own AI tool (been there, done that), we put existing ones on trial to explore where AI fits into our agency’s vision.

To get started, we put together a completely fictional brief for AI to solve. And to keep in line with the sci-fi task, we chose an equally sci-fi product to market: flying cars.

Inspired by Uber’s early ambitions to take to the skies with actual flying taxis (sadly impacted by the pandemic), and the continued efforts of eVTOL companies like Lillium, we launched a fictional brief inviting Artificial Intelligence to have a crack at developing a campaign for “Uber Air”.

The brief: How do we target London-based commuters to consider taking their first-ever Uber Air flight?

The only rule was that we had to use AI for AS MUCH of the strategic and creative process as non-humanly possible.

Here’s what we learned from this fun experiment (besides that flying taxis can transfer you from London’s Heathrow Airport to Manchester Airport in <45 mins)!

Learning #1: LLMs Expedite Learning But Deeper Research Adds Nuance

Large Language Models (LLMs), ChatGPT and Bard, both proved useful in swiftly gaining an overview of new subject matter. They had similar abilities in building out components of our brief such as identifying engagement barriers, product benefits and even channel recommendations. We particularly enjoyed Bard's ability to provide recent data with accompanying sources, enabling us to validate our findings at speed.

BUT we still missed the rigour of studying our audience through quant data (e.g. GlobalWebIndex) and the texture that comes from conducting qualitative research. But it’s exciting to see GlobalWebIndex incorporate GPT into their product, making its data even more accessible. And similarly, Pollfish now utilises AI to automate survey building.

This doesn’t mean you have to choose one or the other – LLMs can be used to crunch through your quant data to make analysis even more efficient… but it’s worth calling out that many of these tools collect the data you provide to train their models. It’s important to seek solutions that help to keep your data private such as ChatGPT Enterprise or Cohere.

Learning #2: LLMs Are Creative Collaborators, Not Directors

ChatGPT and Bard once again impressed us with their ability to quickly generate diverse creative routes, complete with taglines and activation strategies. This time, ChatGPT excelled in wordplay and unconventional concepts like "High-Altitude Boardroom," but overall, we found ourselves frequently hitting ChatGPT’s "regenerate" button to explore a wider range of ideas. This led us to view AI as an invaluable brainstorming partner, an extension of our creative team – a perfect fit for our lean, independent agency. Strategists and Creatives of the human variety still have a valuable role to play in briefing, steering and curating AI’s output.

Learning #3: Generative AI = A Visual Spark, Not A Masterpiece

Generative AI proved useful in rapidly visualising ChatGPTs text based concepts, but some tools were better than others. Adobe Firefly struggled with rendering realistic vehicles (looking more like scrap metal than cars). Midjourney had a tendency to generate inaccurate or nonsensical elements (e.g. multiple Big Bens, London/New York taxi mashups), but after a few iterations, these evolved into much stronger visuals.

Despite these limitations, generative AI has an immense power to rapidly concept visualisations across a variety of design styles. This empowers clients and their marketing teams to better understand and assess the potential of a creative route (arguably in more impactful ways than a scamp or moodboard can achieve) before creative teams invest in developing the final assets.

Here’s a sample of a few creative routes we visualised with Midjourney:

While Midjourney did the vast majority of heavy lifting in order to visualise the ideas, further input from our design team was required to incorporate branding and arrange text-based communications to align with the creative messaging written by ChatGPT.

Learning #4: AI + Data-Driven Marketing = ∞Personalised Creative

While we didn’t take our experiment beyond concept stage, it’s not hard to imagine how the concepts we landed upon could rapidly scale with AI. It’s now possible to generate thousands of AI-powered creative executions, each thoughtfully tailored to individuals by featuring their locale, seasonal or weather data, “casting” culturally relevant AI models, their most popular travel routes etc.

As generative AI matures, its ability to engage global audiences is poised to transform marketing strategies.

At Toaster, for years we have utilised our own data-driven ad scaling product (Niffler) to generate thousands of tailored ad executions, but the ways in which we used to tailor ads had limits (e.g. changing a line of text, changing the background colour, swapping an image within a layout). Now AI has the potential to deliver thousands more hyper-relevant executions with more intelligently customised assets than ever before. You can begin to see how effortlessly advertisers can tailor their assets with Google’s AI-powered Product Studio.

Learning #5: The True Power Of AI Is In Enhancing Processes Experiences

There are always going to be innovations that shake up the creative process – this was true for cloud computing, the introduction of mobile devices and collaboration tools.

Our experiment proved that LLMs and generative AI can positively impact our agency’s creative process to make certain aspects more efficient, rigorous and exploratory. And to summarise our learnings, we created this handy little one-pager, which we’ll endeavour to update regularly (I hope you find it useful too):


The true opportunity for brands using AI lies not in speeding up processes but in making brand interactions more relevant than ever.

Thinking back to our hypothetical Uber Air project, I couldn't help but envision how AI could enhance the end-customer experience.

Imagine an AI agent seamlessly integrating into the lives of business travellers, offering personalised itineraries for their upcoming trips; recommending clever ways to inject moments of relaxation into their busy work schedule. Picture an Uber ride showing up just as you land in a new city via Uber Air. And on the way to your accommodation, Uber Eats is throwing down recommendations for the hottest places to order your next meal from, tying in with local trends, seasonal data, or personal dietary preferences. And all of this content wrapped up in bite-sized, effortlessly tailored messages, thanks to AI's Natural Language Processing capabilities.

The future of AI isn’t about us and our agency’s processes. It’s about how we can meaningfully embed brands into the everyday lives of its consumers. We’re here for those briefs.